摘要
为了对导弹进行多学科设计优化(MDO),建立了包含气动、推进、质量、控制和弹道的多学科分析模型,并采用协作优化对战术导弹多目标多学科设计优化问题进行了表述。针对多目标多学科优化设计问题的计算复杂性,提出了一种新的处理约束多目标优化问题的基于Kriging多目标遗传算法(MOKGA)。MOKGA采用物理规划法将多目标优化转化为单目标优化,然后构建目标函数的考虑约束的EI(Expected Improvement)模型,并采用遗传算法进行求解。将MOKGA与多目标优化算法NSGA-II进行了比较。结果表明,NSGA-II和MOKGA两种算法的优化结果均较初始方案得到明显改进,但MOKGA的精确分析次数较NSGA-II减少了40%,降低了多学科设计优化问题求解过程中的计算复杂性。
In order to design missiles with multidisciplinary design optimization （MDO）, multidisciplinary analysis models are established which involve such diverse fields as aerodynamics, propulsion, mass, control and trajectory. Collaborative optimization is adopted to formulate the MDO of a missile. Due to the computational complexity of the MDO problem, a new method dealing with constrained multiobjective optimization is proposed, which is multiobjective Kriging based genetic algo- rithm （MOKGA）. Physical programming is used in the method to convert the multiobjective to a single objective, and then El （Expected Improvement） is made for the single objective taking into consideration the various constraints. Genetic algorithm is used to optimize the El. Comparison between the multiobjective genetic algorithm NSGA-II and MOKGA is made. The re- sults show that the optimized results of both methods are improved as compared with the initial design, but that MOKGA re- duces by 40% the number of exact analyses as compared with NSGA-II, which reduces remarkably the computational com- plexity of multidisciplinary design optimization.
出处
《航空学报》
EI
CAS
CSCD
北大核心
2013年第11期2557-2564,共8页
Acta Aeronautica et Astronautica Sinica
关键词
导弹
多学科设计优化
多目标
遗传算法
物理规划
missile
multidisciplinary design optimization
multiobjective
genetic algorithm
physical programming
